Titular Professors
Basic knowledge about statistics and probability.
Basic knowledge about calculus.
Learning Outcomes of this subject are:
LO.01 - Know terminology, notation and methods from quantitative research, concretely those related to inference.
LO.02 - Able to analyse and summarize information from lectures and materials provided by the teacher.
LO.03 - Understand and be able to implement ML algorithms in Python.
These are the topics that will be covered during the course:
First part
1. Probability Review
2. Conditional probability and Bayes' Theorem
3. Decision Trees
4. Multicriteria Decision
Second part
5. Multivariable Function analysis
6. Perceptrons
7. Neural Networks
Weekly teaching will consist of one lecturing session to explain basic concepts and group problem-solving in class to apply knowledge to practical situations. Programming sessions are for problem-solving and final project purposes.
Continuous assessment has the following evaluation structure:
Midterm - 30%: Statement, resolution
Final Project - 30%: Statement, resolution, and presentation
Individual assignments grade - 30%: 3 homework
Attendance, participation, and classwork - 10%: Class deliverables
To incorporate the practical case and group grades to the evaluation scheme, the average of the grade of individual assignments must be 4 or above.
The assessment of the practical case will be as follows:
1. Statement: originality and degree of application (10%)
2. Resolution of the exercise: (60%) - 20% each part
3. Conclusions (10%)
4. Explanation of the cases (20%)
RETAKE POLICY: Retake exam will be cumulative and the grade from the retake exam will count as the grade book grade. Maximum grade to pass the course in the retake is 6.0. Individual, group assignment and practical cases must be uploaded to campus virtual before retake exam.
The recommended textbook is:
1) "Decision Analysis for Management Judgment". Paul Goodwin and George Wright. Wiley 2009.
Luckily, the most important references for machine learning are available online.
2) "Neural networks and deep learning". Michael Nielsen, available
3) "Practical Deep Learning for Coders - Practical Deep Learning (fast.ai)", J. Howard, S. Gugger.
For a more formal exposition of the topic and perspectives on advanced ML techniques:
4) "Deep Learning" I. Goodfellow, Y. Bengio, A Courville
https://www.deeplearningbook.org/